Advances in Grid Computing by Zoran Constantinescu

By Zoran Constantinescu

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Other metrics such as resource utilization and task waiting time are important as well, but due to space limitation, results in terms of execution success ratio only are illustrated to evidate that a system can function whilst benefiting from subjective QoS attributes. Figures 4, 5 and 6 show the execution success ratio for the individual and nested tasks on different task types/sizes. The x-axis in all three figures is a configuration state for the QoS attributes considered. e. 5 are considered. e.

J. & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, Vol. 393, No. 6684 (June 1998), pp. 440–442, ISSN (printed): 0028-0836. C. H. (2006). A Simulation-Ga Based Model for Production Planning in Precast Plant, Proceedings of Winter Simulation Conference, pp. 1796 – 1803, ISBN: 1-4244-0500-9, Monterey, CA, Dec. 2006. 2 A Framework for Problem-Specific QoS Based Scheduling in Grids Mohamed Wahib1 , Asim Munawar2 , Masaharu Munetomo3 and Kiyoshi Akama4 1,2 Gradute School of Information Science and Technology, Hokkaido University, Sapporo 3,4 Information Initiative Center, Hokkaido University, Sapporo Japan 1.

1999) propose a framework for QoS in Grid computing, called the Globus Architecture for Reservation and Allocation (GARA), which enables programmers and users to specify and manage end-to end QoS for Grid-based applications. It also provides a uniform mechanism for making QoS reservations for different types of Grid resources, such as processors, networks and storage devices. The main drawback of GARA is its inability to support subtask management, which is one of Grid’s main goals. There are two more drawbacks in GARA: the topology of domain should be known in advance and also the resources can not publish themselves.

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